A novel unsupervised texture image segmentation using a multilayer data condensation spectral clustering algorithm is presented. First, the texture features of each image pixel are extracted by the stationary wavelet transform and a multilayer data condensation method is performed on this texture features data set to obtain a condensation subset. Second, the spectral clustering algorithm based on the manifold similarity measure is used to cluster the condensation subset. Finally, according to the clustering result of the condensation subset, the nearest-neighbor method is adopted to obtain the original image-segmentation result. In the experiments, we apply our method to solve the texture and synthetic aperture radar image segmentation and take self-tuning k-nearest-neighbor spectral clustering and Nyström methods for baseline comparisons. The experimental results show that the proposed method is more robust and effective for texture image segmentation.
Ng-Jordan-Weiss (NJW) method is one of the most widely used spectral clustering algorithms. For a clustering problem
with K clusters, this method clusters data using the largest K eigenvectors of the normalized affinity matrix derived from
the data set. However, the top K eigenvectors are not always the most important eigenvectors for clustering. In this
paper, we propose an eigenvector selection method based on an ensemble of multiple eigenvector rankings (ESEER) for
spectral clustering. In ESEER method, first multiple rankings of eigenvectors are obtained by using the entropy metric,
which is used to measure the importance of each eigenvector, next the multiple eigenvector rankings are aggregated into
a single consensus one, then the first K eigenvectors in the consensus ranking list are adopted as the selected
eigenvectors. We have performed experiments on artificial data sets, standard data sets of UCI repository and
handwritten digits from MNIST database. The experimental results show that ESEER method is more effective than
NJW method in some cases.